Adding Noise to the Hidden Weights: Noise plays a crucial role in regularizing RNNs. Besides regularization, incorporating it has also been shown to improve the model's forecasting horizon yielding better generalization [1]}, [2]}, [3]}, [4]}, [5]}. Motivated by this result, we modify the state update equation further as follows:
\(\mathbf {\hat{z}}_{t-1} = \mathbf {z}_{t-1} + P \mathbf {s}_{t-1}^0 + \mathbf {\epsilon },\quad \forall i, \mathbf {\epsilon }[i] \sim \mathcal {N}(\mu , \sigma ^2)\) 
